4.6k citations · 10k across the 22 of their papers we have counts for
23 papers
Continuous Memory Machines
Ciaran Regan, Kai Arulkumaran, Luke Darlow +3
Recurrent neural networks typically compress information into a single vector-valued recurrent state, forcing short-term computation and long-term retention to share the same repre…
In Search of the Ingredients of Open-Endedness: Replicating Picbreeder with Large Vision-Language Models
Sam Earle, Kai Arulkumaran, Andrew Dai +3
We are in the midst of large-scale industrial and academic efforts to automate the processes of scientific, technological and creative production through AI-driven assistants. Hist…
Preference-Learning Emitters for Mixed-Initiative Quality-Diversity Algorithms
Roberto Gallotta, Kai Arulkumaran, L. B. Soros
In mixed-initiative co-creation tasks, wherein a human and a machine jointly create items, it is important to provide multiple relevant suggestions to the designer. Quality-diversi…
Surrogate Infeasible Fitness Acquirement FI-2Pop for Procedural Content Generation
Roberto Gallotta, Kai Arulkumaran, L. B. Soros
When generating content for video games using procedural content generation (PCG), the goal is to create functional assets of high quality. Prior work has commonly leveraged the fe…
On the link between conscious function and general intelligence in humans and machines
Arthur Juliani, Kai Arulkumaran, Shuntaro Sasai +1
In popular media, there is often a connection drawn between the advent of awareness in artificial agents and those same agents simultaneously achieving human or superhuman level in…
All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RL
Kai Arulkumaran, Dylan R. Ashley, Jürgen Schmidhuber +1
Upside down reinforcement learning (UDRL) flips the conventional use of the return in the objective function in RL upside down, by taking returns as input and predicting actions. U…